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Models of similarity in complex networks
The analysis of networks describing many social, economic, technological, biological and other systems has attracted a lot of attention last decades. Since most of these complex systems evolve over time, there is a need to investigate the changes, which appear in the system, in order to assess the s...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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PeerJ Inc.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280390/ https://www.ncbi.nlm.nih.gov/pubmed/37346584 http://dx.doi.org/10.7717/peerj-cs.1371 |
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author | Shvydun, Sergey |
author_facet | Shvydun, Sergey |
author_sort | Shvydun, Sergey |
collection | PubMed |
description | The analysis of networks describing many social, economic, technological, biological and other systems has attracted a lot of attention last decades. Since most of these complex systems evolve over time, there is a need to investigate the changes, which appear in the system, in order to assess the sustainability of the network and to identify stable periods. In the literature, there have been developed a large number of models that measure the similarity among the networks. There also exist some surveys, which consider a limited number of similarity measures and then perform their correlation analysis, discuss their properties or assess their performances on synthetic benchmarks or real networks. The aim of the article is to extend these studies. The article considers 39 graph distance measures and compares them on simple graphs, random graph models and real networks. The author also evaluates the performance of the models in order to identify which of them can be applied to large networks. The results of the study reveal some important aspects of existing similarity models and provide a better understanding of their advantages and disadvantages. The major finding of the work is that many graph similarity measures of different nature are well correlated and that some comprehensive methods are well agreed with simple models. Such information can be used for the choice of appropriate similarity measure as well as for further development of new models for similarity assessment in network structures. |
format | Online Article Text |
id | pubmed-10280390 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102803902023-06-21 Models of similarity in complex networks Shvydun, Sergey PeerJ Comput Sci Computer Networks and Communications The analysis of networks describing many social, economic, technological, biological and other systems has attracted a lot of attention last decades. Since most of these complex systems evolve over time, there is a need to investigate the changes, which appear in the system, in order to assess the sustainability of the network and to identify stable periods. In the literature, there have been developed a large number of models that measure the similarity among the networks. There also exist some surveys, which consider a limited number of similarity measures and then perform their correlation analysis, discuss their properties or assess their performances on synthetic benchmarks or real networks. The aim of the article is to extend these studies. The article considers 39 graph distance measures and compares them on simple graphs, random graph models and real networks. The author also evaluates the performance of the models in order to identify which of them can be applied to large networks. The results of the study reveal some important aspects of existing similarity models and provide a better understanding of their advantages and disadvantages. The major finding of the work is that many graph similarity measures of different nature are well correlated and that some comprehensive methods are well agreed with simple models. Such information can be used for the choice of appropriate similarity measure as well as for further development of new models for similarity assessment in network structures. PeerJ Inc. 2023-05-02 /pmc/articles/PMC10280390/ /pubmed/37346584 http://dx.doi.org/10.7717/peerj-cs.1371 Text en © 2023 Shvydun https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Computer Networks and Communications Shvydun, Sergey Models of similarity in complex networks |
title | Models of similarity in complex networks |
title_full | Models of similarity in complex networks |
title_fullStr | Models of similarity in complex networks |
title_full_unstemmed | Models of similarity in complex networks |
title_short | Models of similarity in complex networks |
title_sort | models of similarity in complex networks |
topic | Computer Networks and Communications |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280390/ https://www.ncbi.nlm.nih.gov/pubmed/37346584 http://dx.doi.org/10.7717/peerj-cs.1371 |
work_keys_str_mv | AT shvydunsergey modelsofsimilarityincomplexnetworks |